Estimating Spoken Dialog System Quality with User Models [electronic resource] / by Klaus-Peter Engelbrecht.Material type: TextLanguage: English Series: T-Labs Series in Telecommunication Services: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XIV, 127 p. 25 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783642315916Subject(s): Engineering | Computer science | Acoustics in engineering | Telecommunication | Engineering | Signal, Image and Speech Processing | User Interfaces and Human Computer Interaction | Engineering Acoustics | Communications Engineering, NetworksAdditional physical formats: Printed edition:: No titleDDC classification: 621.382 LOC classification: TK5102.9TA1637-1638TK7882.S65Online resources: Click here to access online
MeMo-Usability Workbench -- Evaluation of the Memo User Simulation – Use-Case Inspire Smart Home System -- Detection of Usability Problems Using an Ad-Hoc User Simulation -- Prediction Of User Judgments -- Application of Prediction Models in a Realistic Usage Scenario.
Spoken dialog systems have the potential to offer highly intuitive user interfaces, as they allow systems to be controlled using natural language. However, the complexity inherent in natural language dialogs means that careful testing of the system must be carried out from the very beginning of the design process. This book examines how user models can be used to support such early evaluations in two ways: by running simulations of dialogs, and by estimating the quality judgments of users. First, a design environment supporting the creation of dialog flows, the simulation of dialogs, and the analysis of the simulated data is proposed. How the quality of user simulations may be quantified with respect to their suitability for both formative and summative evaluation is then discussed. The remainder of the book is dedicated to the problem of predicting quality judgments of users based on interaction data. New modeling approaches are presented, which process the dialogs as sequences, and which allow knowledge about the judgment behavior of users to be incorporated into predictions. All proposed methods are validated with example evaluation studies.